Abstract
The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach.
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Buitelaar, P., Cimiano, P., Magnini, B. (eds.): Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence and Applications, vol. 123. IOS Press, Amsterdam (2005)
Muggleton, S., Raedt, L.D.: Inductive logic programming: Theory and methods. J. Log. Program 19/20, 629–679 (1994)
Lisi, F.A., Esposito, F.: Mining the Semantic Web: A logic-based methodology. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 102–111. Springer, Heidelberg (2005)
Hartmann, J., Sure, Y.: A knowledge discovery workbench for the Semantic Web. In: Workshop on Mining for and from the Semantic Web at the ACM SIGKDD (August 2004)
Bloehdorn, S., Sure, Y.: Kernel methods for mining instance data in ontologies. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 58–71. Springer, Heidelberg (2007)
Dánger, R., Ruiz-Shulcloper, J., Llavori, R.B.: Objectminer: A new approach for mining complex objects. In: ICEIS (2), pp. 42–47 (2004)
Rodríguez-González, A.Y., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ruiz-Shulcloper, J.: Mining frequent similar patterns on mixed data. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 136–144. Springer, Heidelberg (2008)
Chi, Y., Muntz, R.R., Nijssen, S., Kok, J.N.: Frequent subtree mining - an overview. Fundam. Inform. 66(1-2), 161–198 (2005)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) ICDM, pp. 313–320. IEEE Computer Society, Los Alamitos (2001)
Kiefer, C., Bernstein, A., Locher, A.: Adding data mining support to SPARQL via statistical relational learning methods. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 478–492. Springer, Heidelberg (2008)
Kochut, K., Janik, M.: SPARQLeR: Extended SPARQL for semantic association discovery. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 145–159. Springer, Heidelberg (2007)
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: SIGMOD Conference, pp. 207–216. ACM Press, New York (1993)
Nebot, V., Llavori, R.B.: Efficient retrieval of ontology fragments using an interval labeling scheme. Inf. Sci. 179(24), 4151–4173 (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
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Nebot, V., Berlanga, R. (2010). Mining Association Rules from Semantic Web Data. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_52
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DOI: https://doi.org/10.1007/978-3-642-13025-0_52
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